DataRobot-Research/drdocs huggingface dataset:
---
title: Version 7.2.0 maintenance releases
description: Maintenance releases for the DataRobot Release 7.2 major release.

---

# Version 7.2.x maintenance releases {: #version-72x-maintenance-releases }

The following maintenance release notes include some fixed issues in the DataRobot Self-Managed AI Platform platform. See also the [features introduced in v7.2.0](v7.2/index), which was introduced _September 13, 2021_.


Version |	Release date
------- | -----------
[v7.2.8](v7.2.8-aml) | _January 26, 2022_
[v7.2.7](v7.2.7-aml) | _December 21, 2021_
[v7.2.6](v7.2.6-aml) | _November 23, 2021_
[v7.2.5](v7.2.5-aml) | _November 9, 2021_
[v7.2.3](v7.2.3-aml) | _October 13, 2021_
[v7.2.2](v7.2.2-aml) | _September 28, 2021_
[v7.2.1](v7.2.1-aml) | _September 19, 2021_
[v7.2.0](v7.2.0-aml) | _September 13, 2021_
---
title: Model Registry
description: How model packages are created and added to the Model Registry, manually or automatically. The Model Registry is an archive of your model packages where you can deploy and share packages.

---

# Model Registry {: #model-registry }

The Model Registry is an organizational hub for the variety of models used in DataRobot. Models are registered as deployment-ready model packages; the registry lists each package available for use. Each package functions the same way, regardless of the origin of its model. In the Model Registry, you can [generate model compliance documentation from model packages](reg-compliance) and [deploy, share, or archive models](reg-action). The Model Registry also contains the [Custom Model Workshop](custom-model-workshop/index), where you can create, deploy, and register custom models.

## Create model packages {: #create-model-packages }

Model packages (model artifacts with associated metadata) can be created [manually](#create-model-packages-manually) or [automatically](#create-model-packages-automatically), depending on the type of model. All model packages are created and stored in the Model Registry, where you can deploy and share them. Model packages are available for any models created in AutoML and prepared for deployment.

### Create model packages manually {: #create-model-packages-manually }

The following sections describe the steps necessary for manually creating model packages for custom inference models and external models. Custom inference models are [created and tested](custom-inf-model) in the Custom Model Workshop, and external models operate outside of DataRobot.

Manual model package creation:

* [Register DataRobot models](dr-model-reg)
* [Register custom inference models](custom-model-reg)
* [Register external models](ext-model-reg)

### Create model packages automatically {: #create-model-packages-automatically }

The following sections describe the steps necessary to trigger the automatic creation of model packages for [custom inference models](custom-inf-model) and models provided when [adding a new deployment](deploy-methods/index).

Automatic model package creation:

* Deploy a custom model
* Create a deployment via the “Add deployment” action on the Deployments page

####  Deploy a custom model {: #deploy-a-custom-model }

When you [deploy a custom model](deploy-custom-inf-model), DataRobot automatically creates a model package, which you can access in the **Model Registry** under the **Model Packages** tab. The deployment you create also uses this model package.

####  Deploy from the inventory {: #deploy-from-the-inventory }

When you [create a new deployment](deploy-methods/index) with any type of model, DataRobot automatically creates a model package for the model being deployed. You can access it in the **Model Registry** under the **Model Packages** tab.
# Version 7.2.1 {: #version-721 }

_September 19, 2021_

The DataRobot v7.2.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:

* [Features introduced in v7.2.0](v7.2.0-aml)

## Issues fixed in v7.2.1 {: #issues-fixed-in-v721 }

The following issues have been fixed since Enterprise release v7.2.0:

### Enterprise {: #enterprise}

* EP-1724: Fixes a mongo-watch issue where Replica Set(RS) would kick out of mongo during the initial RS setup.
* EP-1307: Comments out `PYTHON3_SERVICES` in example configs for cluster installs so that it can be appropriately enabled for new installs where supported.

### Platform {: #platform}

* PLT-4386: The following script was added: `./sbin/datarobot-manage-queue`. The script allows recovering the stuck jobs after DB backup and restore on the enterprise environment.

### MLOps {: #mlops}

* RAPTOR-6334: Fixes an issue where the execution environment version responses in the API only included non-null links in the environment version's downloadable artifacts if they are downloadable.
* RAPTOR-6267: Fixes an issue when applying database migration on upgrade from 7.1 to 7.2 when storage abstraction is not available. 

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
# Version 7.0.1 {: #version-701 }

_March 29, 2021_

The DataRobot v7.0.1 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.0.0 release notes for:

* [Features introduced in v7.0.0](v7.0.0-aml)

## Issues fixed in v7.0.1 {: #issues-fixed-in-v701 }

The following issues have been fixed since Enterprise release v7.0.0:

### Enterprise {: #enterprise }

* EP-951: Fixes an issue when upgrading to the latest version of DataRobot with the TimescaleDB service used in 6.2 and older. Now, upgrades run as expected.
* EP-872: Allows ingestion of Parquet files via "Enable conversion of binary files inside worker process" when `read_only_containers` is `True`.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Governance workflow support for Feature Discovery deployments
description: DataRobot's Feature Discovery projects support the deployment approval workflow to control updates to secondary dataset configurations.
section_name: Data
maturity: public-preview
---

# Governance workflow support for Feature Discovery deployments {: #governance-workflow-support-for-feature-discovery-deployments }

!!! info "Availability information"
    The governance workflow for Feature Discovery deployments, available as a public preview feature, is off by default. Contact your DataRobot representative or administrator for information on enabling the feature.

    <b>Feature flags:</b> Enable Feature Discovery Global Approval Workflow for Model Deployments, Enable Global Approval Workflow, Can manage Global Approval Policies, Enable MLOps

Feature Discovery projects now support the [deployment approval workflow](dep-admin) to control updates to secondary dataset configurations. Before controlling secondary datasets with the approval workflow, an MLOps admin must go to **User Settings > Approval Policies** and set up the "Secondary datasets configuration changed" [approval policy](deploy-approval) trigger.

![](images/feat-disc-app-2.png)

When the trigger is configured, [changing a secondary dataset](fd-predict#secondary-datasets-configurations-modal) in **Deployments > Settings** prompts the creation of a change request.

![](images/feat-disc-app-3.png)

As the creator of a change request, there will be a pending changes notification displayed at the top of **Deployments > Overview** and its status is listed under **History**.

![](images/feat-disc-app-1.png)

As a reviewer of a change request, there will be a pending changes notification displayed at the top of **Deployments > Overview** with the option to [**Add Review**](dep-admin).

![](images/dep-admin-2.png)
---
title: Use Case reference
description: Answers questions and provides tips for working with Use Cases in DataRobot's Workbench.

---

# Use Case reference {: #use-case-reference }

??? faq "How do you move between Workbench and DataRobot Classic?"
	You can easily switch back and forth between Workbench and DataRobot Classic. Access Workbench from the top navigation bar in the application.

	![](images/wb-uc-faq-1.png)

	To return to DataRobot Classic, choose a specific location from the dropdown the top navigation bar.

	![](images/wb-uc-faq-2.png)


??? faq "Are Use Cases available in DataRobot Classic?"
	  When you create experiments in Workbench, you can access them in DataRobot Classic via the [project management center](manage-projects#manage-projects-control-center){ target=_blank }. When you select an experiment from Classic that was created in Workbench, you will see the Use Case name above the dataset name, indicating it is available in Workbench.

	  ![](images/wb-uc-faq-3.png)


??? faq "Is a Use Case just a folder?"
	No, it's more. Use Cases organize, provide a permission control mechanism, and are a collaborative space where teams can comment and review each other’s work. Use Cases make sure that your ML projects deliver actual business value.

??? faq "Are DataRobot Classic projects available in Workbench?"
	Projects originally created in Classic are not currently available in Workbench. Once all the advanced features of DataRobot Classic are available, they will be eligible to migrate into Workbench.

??? faq "What types of experiments are supported in Workbench?"
	Currently Workbench supports binary classification and regression projects. Because development is ongoing, see the release notes for a full list of supported capabilities.

??? faq "What does Workbench offer that is not available in DataRobot Classic?"
	Workbench offers a streamlined Workflow that includes native data prep, and Use Cases to organize, compare, and share projects. Coming soon will be an opportunity to compare models from different experiments (but within the same use case) from a single page.

??? faq "Is Workbench available for code-first users?"
	Just like other DataRobot offerings, the Workbench pipeline is fully supported with the Python API. It can be authored with any Python IDE or notebook, but provides an even more seamless experience with DataRobot’s own notebook solution. Notebook integration provides ways to prepare data and modeling with a code-first approach and these capabilities will provide the same functionality as is available in the GUI.
---
title: Platform
description: This section includes information and links for managing user settings; authentication and SSO; the administrator's guide; sharing and permissions; user documentation for companion tools; and more.
---

# Platform {: #platform }

The platform section provides materials for users and administrators to manage their DataRobot accounts.

!!! note
    DataRobot performs service maintenance regularly. Although most maintenance will occur unnoticed, some may cause a temporary impact. Status page announcements provide information on service outages, scheduled maintenance, and historical uptime. You can view and subscribe to notifications from the [DataRobot status page](https://status.datarobot.com/){ target=_blank }.

Topic | Describes...
----- | ------
[Account management](account-mgmt/index) | View information to help manage your DataRobot account.
[Authentication](authentication/index) | Learn about authentication in DataRobot, including SSO, 2FA, and stored credentials.
[Administrator's guide](admin-guide/index) | For administrators, get help in managing the DataRobot application.
[Data and sharing](data-sharing/index) | Learn about sharing, permissions, and data file size requirements.
[Companion tools](companion-tools/index) | Access user documentation for Algorithmia and Paxata Data Prep.

## Browser compatibility {: #browser-compatibility }

{% include 'includes/browser-compatibility.md' %}
---
title: Data connections
description: Connect to various data sources and manage stored credentials.

---

# Data connections {: #data-connections }

The AI Catalog is a browsable and searchable collection of registered objects that contains definitions and relationships between various object types. These definitions and relationships include data connections, data sources, data metadata, and blueprints.

Topic | Describes...
----- | ------
[Stored data credentials](stored-creds) | Add and manage securely stored credentials for reuse in accessing secure data sources.
[Share secure configurations](secure-config) | IT admins can configure OAuth-based authentication parameters for a data connection, and then securely share them with other users without exposing sensitive fields.
[Connect to data sources](data-conn) | Set up database connections using a “self-service” JDBC platform.
[Register data in the AI Catalog from a data connection](catalog#add-data-from-external-connections) | Register data in the catalog from a new or existing data connection.
[Supported databases](data-sources/index) | View a list of supported and deprecated databases, as well as the required parameters to connect to them in DataRobot.
# Version 8.0.2 {: #version-802 }

_April 30, 2022_

The DataRobot v8.0.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

## Issues fixed in v8.0.2 {: #issues-fixed-in-v802 }

The following issues have been fixed since Enterprise release v8.0.1:

### Platform {: #platform }

* DM-5972: List schemas for BigQuery JDBC data connections now only return items from the current catalog asset or project. This fix also speeds up list tables by restricting to the current project if the schema is specified but the catalog asset is not.
* PLT-6559: Enhanced global SAML SSO management now generally available for 8.0.x.

### MLOps {: #mlops }

* MMM-9371: Fixes an issue that prevented enabling the "Enable ability to reset deployment statistics" feature flag for Self-Managed AI Platform installations.
* RAPTOR-7475: The UID and GID for Custom Inference model containers are now configurable (`LONG_RUNNING_SERVICES_SECURITY_CONTEXT_USER_ID`, `LONG_RUNNING_SERVICES_SECURITY_CONTEXT_GROUP_ID`) and no longer fixed to `1000`.
* RAPTOR-7478: Fixes an issue with date column handling in Compliance Docs for Custom Inference Models. Date columns will are now excluded from Feature Fit and Feature Effects.

### Time series {: #time-series }

* TIME-10937: Fixes an issue preventing users from creating a time series anomaly detection project with datasets larger than 800 MB via the UI.

### Visual AI {: #visual-ai }

* VIZAI-3312: Fixes an issue with CSV and PNG exports for external datasets confusion charts.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Tutorials
description: Tutorials provide quick, task-based instructions for success in modeling.

---

# Tutorials {: #tutorials }

DataRobot offers a variety of tutorials to assist you in using different aspects of the application, outlined below:

Topic | Describes how to...
----- | ------
[Prepare learning data](prep-learning-data/index) | Assemble, curate, and prepare data for use with DataRobot
[Create AI models](creating-ai-models/index) | Execute common tasks related to modeling and DataRobot projects
[Explore AI insights](explore-ai-insights/index) | Analyze model-specific insights generated by DataRobot
# kdb+ {: #kdb }

## Supported authentication {: #supported-authentication }

- Username/password

## Prerequisites {: #prerequisites }

The following is required before connecting to kdb+ in DataRobot:

- kdb+ account

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with kdb+:

Required field | Description |  Documentation
--------------- | ---------- |  -----------
`address`       | The connection URL used to connect to kdb+.<br><br>**Example:**<br> `jdbc-cert-kdb+.cqz9ezythbf4.us-east-1.rds.amazonaws.com:3306`  |   [kx documentation](https://code.kx.com/q/interfaces/jdbc-client-for-kdb/){ target=_blank }

{% include 'includes/data-conn-trouble.md' %}
---
title: Deployment reports
description: Learn about the deployment reports, which summarize details of a deployment, such as its owner, how the model was built, the model age, and the humility monitoring status.

---

# Deployment reports {: #deployment-reports }

Ongoing monitoring reports are a critical step in the deployment and governance process. DataRobot allows you to download deployment reports from MLOps, compiling deployment status, charts, and overall quality into a sharable report. Deployment reports are compatible with all deployment types.

## Generate a deployment report {: #generate-a-deployment-report }

To generate a report for a deployment, select it from the inventory and navigate to the **Overview** tab.

1. Select **Generate Report now**.

    ![](images/dep-rep-1.png)

2. Configure the report settings. Select the model used, the date range, and the date resolution (the granularity of comparison for the deployment's statistics within the specified time range). Once configured, click **Generate report**.

    ![](images/dep-rep-2.png)

3. Allow some time for the report generation process to complete. Once finished, select the eye icon to view the report in your browser, or the download icon to view it locally.

    ![](images/dep-rep-3.png)

## Schedule deployment reports {: #schedule-deployment-reports }

In addition to manual creation, DataRobot allows you to manage a schedule to generate deployment reports automatically: 

1. In a deployment, click the **Notifications** tab.

2. Select **Create new report schedule**.

    ![](images/dep-rep-4.png)

3. Complete the fields to fully configure the schedule:

    ![](images/dep-rep-5.png)

    |                        | Field       | Description |
    |------------------------|-------------|-------------|
    | ![](images/icon-1.png) | Frequency  | The cadence at which deployment reports are generated for the deployment. |
    | ![](images/icon-2.png) | Time       | The time at which the deployment report is generated. |
    | ![](images/icon-3.png) | Range      | The period of time captured by the report. |
    | ![](images/icon-4.png) | Resolution | The granularity of comparison for the deployment's statistics within the specified time range. |
    | ![](images/icon-5.png) | Additional recipients | Optional. The email addresses of those who should receive the deployment report, in addition to those who have access to the deployment. |

4. After fully determining the report schedule, click **Save report schedule**. 

    The reports automatically generate at the configured dates and times.
This page describes the coding intelligence features included in the code cells of DataRobot Notebooks.

### Code snippets {: #code-snippets }

DataRobot provides a set of pre-defined code snippets, inserted as cells in a notebook, for commonly used methods in the DataRobot API as well as other data science tasks. These include connecting to external data sources, deploying a model, creating a model factory, and more. Access code snippets by selecting the code icon in the sidebar.

![](images/nb-14.png)

### Docstrings reference {: #docstrings-reference }

When using a specific method or class, use the `Shift + Tab` keyboard shortcut directly from the code editor to query docstrings. Additional documentation appears as an overlay.

![](images/nb-13.png)

### Autocomplete {: #autocomplete }

{% if section(page, "dr-notebooks") %}

When [editing code](dr-action-nb#modal-editor), you can autocomplete lines using the **Tab** key. To activate autocompletion, you must first [configure and start the notebook environment](dr-env-nb).

{% else %}

When [editing code](wb-action-nb#modal-editor), you can autocomplete lines using the **Tab** key. To activate autocompletion, you must first [configure and start the notebook environment](wb-env-nb).

{% endif %}

### Syntax highlighting {: #syntax-highlighting }

When writing code in code cells, DataRobot highlights syntax for the language set in the notebook environment. Below is a sample of syntax highlighting for Python:

![](images/nb-27.png)
---
title:  Remote repository file browser for custom models and tasks
description: The remote repository file browser for custom models and tasks allows you to browse a remote repository from the Custom Model Workshop and select the files you want to pull into a custom model or task.
section_name: MLOps
maturity: public-preview
---

# Remote repository file browser for custom models and tasks {: #remote-repository-file-browser-for-custom-models-and-tasks }

!!! info "Availability information"
    The remote repository file browser for custom models and tasks is off by default. Contact your DataRobot representative or administrator for information on enabling this feature.

    <b>Feature flag:</b> Enable File Browser for Pulling Model or Task Files from Remote Repositories

Now available as a public preview feature, you can browse the folders and files in a remote repository to select the files you want to add to a custom model or task. When you [add a model](custom-inf-model#create-a-new-custom-model) or [add a task](cml-custom-tasks) to the Custom Model Workshop, you can add files to that model or task from a wide range of repositories, including Bitbucket, GitHub, GitHub Enterprise, S3, GitLab, and GitLab Enterprise. After you [add a repository to DataRobot](custom-model-repos#add-a-remote-repository), you can pull files from the repository and include them in the custom model or task.

To use the remote repository file browser:

1. In the top navigation bar, click **Model Registry**.

2. Click **Custom Model Workshop**, click either the **Models** or **Tasks** tab, and select a model or task from the list.

3. Under **Assemble Model** or **Create Estimator** (in the Tasks tab), click **Select remote repository**.

    ![](images/pp-select-remote-repo.png)

    !!! note
        If the **Model** or **Estimator** group box (in the Tasks tab) is empty, select a **Base Environment** for the model or task.

4. In the **Select a remote repository** dialog box, select a repository in the list and click **Select content**.

    ![](images/custom-repo-6.png)

5. In the **Pull from GitHub repository** dialog box, select the checkbox for any files or folders you want to pull into the custom model.

    In addition, you can click **Select all** to select every file in the repository, or, after you select one or more files, you can click **Deselect all** to clear your selections.

    !!! note
        This step uses GitHub as an example; however, the process is the same for each repository type. 

    ![](images/pp-custom-model-repo-browse.png)

    !!! tip
        You can see how many files you have selected at the bottom of the dialog box (e.g., _+ 4 files will be added_).

6. Once you select each file you want to pull into the custom model, click **Pull**.
    
    The added files appear under the **Model** header as part of the custom model.